Language Localization From Magnetoencephalography (MEG) Beta‐Power Dynamics During Sentence Completion
Bibliographic record
Abstract
For noninvasive language mapping, the choice of imaging method, task, and baseline remains an area of active research. While the sentence completion task is a recommended option for fMRI studies, the indirect nature of the signal is a limitation of the imaging method. This study presents a sentence completion paradigm for group- and individual-level language localization and lateralization based on beta power (17-25 Hz) modulations. MEG recordings of 21 neurologically healthy native Russian speakers were used to test whether the task would elicit beta desynchronization in canonical language regions during sentence completion. In addition to the traditional passive (no-task) control condition, an active (syllable repetition) control condition was used to further control for nonrelevant processes. The paradigm revealed the engagement of anterior and posterior language-related brain areas using both active and passive control conditions. However, the active control condition provided more widespread activity patterns, suggesting its superior suitability for further individual presurgical language mapping. Despite the individual variability in the results, their general agreement with the current understanding of the language-associated brain topography supports the potential of the developed MEG paradigm for presurgical language mapping.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".